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已发表论文
重症 COVID-19 患者脓毒性休克的早期预测：列线图模型的开发与验证
Authors Jin Y, Ma J, Zhou W, Lu M
Received 19 October 2025
Accepted for publication 6 February 2026
Published 12 February 2026 Volume 2026:19 575280
DOI https://doi.org/10.2147/IDR.S575280
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Chi H. Lee
Yinbing Jin,1,* Junbao Ma,1,* Wenhan Zhou,1 Mingfeng Lu1,2
1Emergency Department, The Yangzhou Clinical Medical College of Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China; 2Emergency Department, Northern Jiangsu People’s Hospital, Yangzhou, Jiangsu, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Mingfeng Lu, Email yzlmf2011@163.com
Purpose: Septic shock is a severe complication in critically ill patients with COVID-19, often associated with poor prognosis. Predictive factors for septic shock remain undetermined. Our objective was to develop an early predictive model for septic shock in severe COVID-19 patients to assist emergency and critical care physicians in resource allocation and medical decision-making.
Patients and Methods: The training cohort was sourced from the cases admitted to Northern Jiangsu People’s Hospital between December 2022 and February 2023, while the validation cohort was retrieved from the MIMIC-IV dataset. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was used to screen for predictors. A multivariate logistic regression was employed to build the predictive model, which was then represented as a nomogram. The performance of the nomogram was evaluated using the Receiver Operating Characteristic (ROC) curve, calibration plot, and Decision Curve Analysis (DCA). External validation was conducted by assessing the model’s performance in the validation cohort.
Results: A collective of 274 patients and 75 patients were respectively enrolled as the training cohort and the validation cohort in this study. The predictors included in the nomogram were albumin, mean arterial pressure, lactate, and the Sequential Organ Failure Assessment (SOFA) score. The area under the ROC curve (AUC) for the modeling set was 0.800 (95% CI 0.741– 0.858), and for the validation set, it was 0.775 (95% CI 0.651– 0.899). Additionally, the calibration curve indicated a correlation between predicted and observed outcomes, and DCA highlighted the clinical utility of the nomogram.
Conclusion: We developed and validated a diagnostic nomogram model for septic shock in critically ill COVID-19 patients, incorporating four parameters: SOFA score, albumin, mean arterial pressure, and lactate. This model demonstrates significant potential in predicting septic shock among critically ill COVID-19 patients.
Keywords: severe COVID-19, septic shock, nomogram, predictive model
1Emergency Department, The Yangzhou Clinical Medical College of Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China; 2Emergency Department, Northern Jiangsu People’s Hospital, Yangzhou, Jiangsu, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Mingfeng Lu, Email yzlmf2011@163.com
Purpose: Septic shock is a severe complication in critically ill patients with COVID-19, often associated with poor prognosis. Predictive factors for septic shock remain undetermined. Our objective was to develop an early predictive model for septic shock in severe COVID-19 patients to assist emergency and critical care physicians in resource allocation and medical decision-making.
Patients and Methods: The training cohort was sourced from the cases admitted to Northern Jiangsu People’s Hospital between December 2022 and February 2023, while the validation cohort was retrieved from the MIMIC-IV dataset. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was used to screen for predictors. A multivariate logistic regression was employed to build the predictive model, which was then represented as a nomogram. The performance of the nomogram was evaluated using the Receiver Operating Characteristic (ROC) curve, calibration plot, and Decision Curve Analysis (DCA). External validation was conducted by assessing the model’s performance in the validation cohort.
Results: A collective of 274 patients and 75 patients were respectively enrolled as the training cohort and the validation cohort in this study. The predictors included in the nomogram were albumin, mean arterial pressure, lactate, and the Sequential Organ Failure Assessment (SOFA) score. The area under the ROC curve (AUC) for the modeling set was 0.800 (95% CI 0.741– 0.858), and for the validation set, it was 0.775 (95% CI 0.651– 0.899). Additionally, the calibration curve indicated a correlation between predicted and observed outcomes, and DCA highlighted the clinical utility of the nomogram.
Conclusion: We developed and validated a diagnostic nomogram model for septic shock in critically ill COVID-19 patients, incorporating four parameters: SOFA score, albumin, mean arterial pressure, and lactate. This model demonstrates significant potential in predicting septic shock among critically ill COVID-19 patients.
Keywords: severe COVID-19, septic shock, nomogram, predictive model